Practical, Non-Invasive Measurement of Urinary Catheter Insertion Forces and Motions

Safdari, Amer; Ling, Xiaoyin; Tradewell, Michael B.; Kowalewski, Timothy M.; Sweet, Robert

doi:10.1115/dmd2019-3308

Cited by 3 publications

(5 citation statements)

References 4 publications

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“…This agrees with the observations made in Rosen et al [1], Kerdok et al [4], and Brouwer et al [5], which all observed significant changes from in vivo to ex vivo tissues. This also agrees with Safdari et al [6], which found that post-freeze liver and spleen tissue exhibited measurable, statistically significant lower stiffnesses compared to post mortem tissue.…”

Section: Discussionsupporting

confidence: 92%

“…However, the authors did cite that the lack of statistical significance could potentially be due to large variability in the data, thus further analysis could yield differing results. The major outcomes of this current study, as well as related outcomes of the previously discussed studies [1][2][3][4][5][6], are summarized below in Table 4.…”

Section: Discussionmentioning

confidence: 99%

“…Prior work in this project published by Safdari in 2019 [6] investigated characterizing tissue mechanical response in different states. This study only analyzed the tissues in the post mortem, to ex vivo, to post-freeze tissue states.…”

Section: Plos Onementioning

confidence: 99%

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Variability of tissue mechanical response in Sus Domesticus porcine models from in vivo to ex vivo conditions

et al. 2023

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Background Healthcare simulators have been demonstrated to be a valuable resource for training several technical and nontechnical skills. A gap in the fidelity of tissues has been acknowledged as a barrier to application for current simulators; especially for interventional procedures. Inaccurate or unrealistic mechanical response of a simulated tissue to a given surgical tool motion may result in negative training transfer and/or prevents the “suspension of disbelief” necessary for a trainee to engage in the activity. Thus, where it is relevant to training outcomes, there should be an effort to create healthcare simulators with simulated tissue mechanical responses that match or represent those of biological tissues. Historically, this data is most often gathered from preserved (post mortem) tissue; however, there is a concern that the mechanical properties of preserved tissue, that lacks blood flow, may lack adequate accuracy to provide the necessary training efficacy of simulators. Methods and findings This work explores the effect of the “state” of the tissue testing status on liver and peritoneal tissue by using a customized handheld grasper to measure the mechanical responses of representative porcine (Sus domesticus) tissues in n = 5 animals across five test conditions: in vivo, post mortem (in-situ), ex vivo (immediately removed from fresh porcine cadaver), post-refrigeration, and post-freeze-thaw cycle spanning up to 72 hours after death. No statistically significant difference was observed in the mechanical responses due to grasping between in vivo and post-freeze conditions for porcine liver and peritoneum tissue samples (p = 0.05 for derived stiffness at grasping force values F = 5N and 6.5N). Furthermore, variance between in vivo and post-freeze conditions within each animal, was comparable to the variance of the in vivo condition between animals. Conclusions Results of this study further validate the use of preserved tissue in the design of medical simulators via observing tissue mechanical responses of post-freeze tissue comparable to in vivo tissue. Therefore, the use of thawed preserved tissue for the further study and emulation of mechanical perturbation of the liver and peritoneum can be considered. Further work in this area should investigate these trends further, particularly in regard to other tissues and the potential effects varying preservation methods may yield.

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Section: Discussionsupporting

confidence: 92%

Section: Discussionmentioning

confidence: 99%

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Variability of tissue mechanical response in Sus Domesticus porcine models from in vivo to ex vivo conditions

et al. 2023

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“…The device used for all data collection was a scissor-like tissue grasping device designed by the Medical Robotics and Devices lab. A detailed discussion of the grasping device and its measurement performance is described elsewhere (8). The device contains two opposing load cells (TAL220B, HT Sensor Technology) in a scissor-like fashion, with 3D-printed hemispherical plastic tips serving as the grasping surface and a quadrature rotary encoder (AMT102-V, CUI Inc.) to measure the angle of the arms as the device opens to monitor tissue thickness and overall displacement.…”

Section: Methodsmentioning

confidence: 99%

“…Prior work in this project published by Safdari in 2019 (8) investigated characterizing tissue mechanical response in different states. While the study only analyzed the tissues in the postmortem , to ex vivo , to post-freeze tissue states, it found that post-freeze liver and spleen tissue exhibited measurable, statistically significant lower stiffnesses compared to postmortem tissue.…”

Section: Introductionmentioning

confidence: 99%

Variability of Tissue Mechanical Response in Sus Domesticus Porcine Models from in vivo to ex vivo Conditions

Malik

Drahos

Safdari

et al. 2022

Preprint

Self Cite

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Healthcare simulators have been demonstrated to be a valuable resource for training several technical and nontechnical skills. A gap in the fidelity of tissues has been acknowledged as a barrier to application for current simulators; especially for interventional procedures. Inaccurate or unrealistic mechanical response of a simulated tissue to a given surgical tool motion may result in negative training transfer and/or prevents the “suspension of disbelief” necessary for a trainee to engage in the activity. Thus, where it is relevant to training outcomes, there should be an effort to create healthcare simulators with simulated tissue mechanical responses that match or represent those of biological tissues. Historically, this data is most often gathered from preserved (post mortem) tissue; however, there is a concern that the mechanical properties of preserved tissue, that lacks blood flow, may lack adequate accuracy to provide the necessary training efficacy of simulators. This work explores the effect of the “state” of the tissue testing status on liver and peritoneal tissue by using a customized handheld grasper to measure the mechanical responses of representative porcine (Sus domesticus) tissues in N=5 animals across five test conditions: in vivo, postmortem (in-situ), ex vivo (immediately removed from fresh porcine cadaver), post-refrigeration, and post-freeze-thaw cycle spanning up to 72 hours after death. No statistically significant difference was observed in the mechanical responses due to grasping between in vivo and post-freeze conditions for porcine liver and peritoneum tissue samples (p = 0.05 for derived stiffness at grasping force values F = 5N and 6.5N). Furthermore, variance between in vivo and post-thaw conditions within each animal, was comparable to the variance of the in vivo condition between animals. Thus, the use of thawed, preserved tissue for the further study and emulation of mechanical perturbation of the liver and peritoneum can be considered.

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Robot-Assisted Augmented Reality (AR)-Guided Surgical Navigation for Periacetabular Osteotomy

Ding,

Sun,

Zheng

2024

Sensors

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Periacetabular osteotomy (PAO) is an effective approach for the surgical treatment of developmental dysplasia of the hip (DDH). However, due to the complex anatomical structure around the hip joint and the limited field of view (FoV) during the surgery, it is challenging for surgeons to perform a PAO surgery. To solve this challenge, we propose a robot-assisted, augmented reality (AR)-guided surgical navigation system for PAO. The system mainly consists of a robot arm, an optical tracker, and a Microsoft HoloLens 2 headset, which is a state-of-the-art (SOTA) optical see-through (OST) head-mounted display (HMD). For AR guidance, we propose an optical marker-based AR registration method to estimate a transformation from the optical tracker coordinate system (COS) to the virtual space COS such that the virtual models can be superimposed on the corresponding physical counterparts. Furthermore, to guide the osteotomy, the developed system automatically aligns a bone saw with osteotomy planes planned in preoperative images. Then, it provides surgeons with not only virtual constraints to restrict movement of the bone saw but also AR guidance for visual feedback without sight diversion, leading to higher surgical accuracy and improved surgical safety. Comprehensive experiments were conducted to evaluate both the AR registration accuracy and osteotomy accuracy of the developed navigation system. The proposed AR registration method achieved an average mean absolute distance error (mADE) of 1.96 ± 0.43 mm. The robotic system achieved an average center translation error of 0.96 ± 0.23 mm, an average maximum distance of 1.31 ± 0.20 mm, and an average angular deviation of 3.77 ± 0.85°. Experimental results demonstrated both the AR registration accuracy and the osteotomy accuracy of the developed system.

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Practical, Non-Invasive Measurement of Urinary Catheter Insertion Forces and Motions

Cited by 3 publications

References 4 publications

Variability of tissue mechanical response in Sus Domesticus porcine models from in vivo to ex vivo conditions

Variability of tissue mechanical response in Sus Domesticus porcine models from in vivo to ex vivo conditions

Variability of Tissue Mechanical Response in Sus Domesticus Porcine Models from in vivo to ex vivo Conditions

Robot-Assisted Augmented Reality (AR)-Guided Surgical Navigation for Periacetabular Osteotomy

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